Contoso, Ltd. is a US-based health supplements company with two divisions: Sales and Research. The Sales division comprises the Online Sales and Retail Sales departments, while the Research division assigns product lines to individual teams. The Sales division leverages a Microsoft Power BI Premium capacity, whereas the Research division uses an on-premises, third-party data warehousing solution. Fabric is enabled for contoso.com, and the Research division's data is stored in Azure Data Lake Storage Gen2. Contoso intends to enable Fabric support in Power BI Premium for the Sales division, make all data accessible in Fabric, and utilize dedicated, on-demand capacity for the Research division workspaces. The semantic models within the Research division workspaces must operate in Direct Lake mode.
You are tasked with refreshing the Orders table of the Online Sales department. The solution must adhere to the semantic model requirements, specifically minimizing the number of rows added to the Orders table during each refresh. What should you include in the solution?
A
an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the maximum value of the OrderID column in the destination lakehouse
B
an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the minimum value of the OrderID column in the destination lakehouse
C
an Azure Data Factory pipeline that executes a dataflow to retrieve the minimum value of the OrderID column in the destination lakehouse
D
an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderID column in the destination lakehouse
Explanation:
To meet the requirement of minimizing the number of rows added during refreshes, incremental loading should be used. Incremental loading can be achieved by retrieving the maximum OrderID value from the destination lakehouse, as this allows the system to determine new rows that need to be added since the last refresh. Azure Data Factory dataflows can be used for this purpose because they support operations to retrieve the maximum value from a column, enabling efficient incremental loading. Therefore, the correct option is an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderID column in the destination lakehouse.
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